CN112787322A - Dynamic management method for power grid based on scada system and multiple time scales - Google Patents
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
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- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
- H02J3/16—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by adjustment of reactive power
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Abstract
The invention discloses a dynamic power grid management method based on a scada system and multiple time scales. The dynamic evolution method analyzes the time distribution dynamic change along with the real-time state of the power grid from the large time scale, can better depict the dynamic evolution rule of the power grid, effectively avoids the influence of random events, provides a new scheme for the real-time dynamic management of the power grid through the random process models of power, voltage and reactive power and voltage established under the small time scale, and is beneficial to the construction of the ubiquitous power internet of things.
Description
Technical Field
The invention belongs to the optimization technology of a power grid, and particularly relates to a dynamic management method of the power grid based on a scada system and multiple time scales.
Background
With the large-scale access of multi-power supply and new energy, along with the development of ubiquitous power internet of things engineering, the scale of a power grid is continuously enlarged, the change rule of power, reactive power and voltage in a source-mounted line is increasingly complex, and in order to realize the optimal utilization of energy resources in the whole power grid, reduce the fluctuation of system voltage, relevant research is different in month and new day.
The SCADA system is a data acquisition and monitoring control system. The SCADA system is a DCS and electric power automatic monitoring system based on a computer; the method is currently applied to a plurality of fields such as data acquisition, monitoring control, process control and the like in the fields of electric power, metallurgy, petroleum, chemical industry, gas, railways and the like. In an electric power system, an SCADA system plays an important role in a telemechanical system, can monitor and control on-site operating equipment so as to realize various functions of data acquisition, equipment control, measurement, parameter adjustment, various signal alarms and the like, and plays a very important role in the comprehensive automation construction of the existing transformer substation.
The power and reactive data on the main network terminal in the source load circuit depict the electricity consumption demand of residents. From a long time scale, the power demand reflects the overall power demand of the region, and has certain stability. From the real-time scale, the power demand also reflects the individual power demand of each user, and the method has strong randomness. Starting from a view point of a multi-time scale, an analysis method for a dynamic evolution rule among power, reactive power and voltage on a source line is constructed according to real-time scada sampling data on a main network terminal, and the method is a new requirement for guaranteeing the safety of the whole power grid on source line analysis and big data processing.
Disclosure of Invention
The purpose of the invention is as follows: in order to solve the problem that the real-time dynamic power demand influences the stable and safe operation of a power grid, the invention provides a dynamic power grid management method based on a scada system and multiple time scales.
The technical scheme is as follows: a dynamic management method of a power grid based on a scada system and multiple time scales is characterized in that real-time data are obtained based on the scada system on a main network terminal in a source line, then dynamic analysis models of the power grid are respectively established by taking one month as a large time scale and 5 seconds as a small time scale, data under the same state window are subjected to superposition and filtering processing based on a majority theorem under the large time scale, an analysis model is established by a difference method and an autoregressive moving average model ARIMA under the small time scale, and finally the power grid is managed according to dynamic relations between active power, reactive power and voltage.
The method comprises the following steps:
(1) acquiring scada system sampling data of a natural month on a power distribution network terminal, and establishing dynamic time windows of different states under a large time scale according to time distribution characteristics of real-time data of power, reactive power and voltage in a daily power grid;
(2) under a large time scale, data under the same state window are subjected to superposition and filtering processing, and function expressions of dynamic relations among power, reactive power and voltage in different states are respectively established;
(3) establishing a two-dimensional time sequence of power and voltage under a small time scale, establishing a summation autoregressive moving average model ARIMA according to a difference method, and identifying model parameters under a real-time scale to obtain a mathematical model between the power and the voltage;
(4) establishing a reactive power and voltage two-dimensional time sequence from a large time scale and a small time scale;
(5) and (5) repeating the step (4) to obtain a mathematical model between the reactive power and the voltage under the small time scale.
Further, the time scale of the large time scale in the step (2) is one month, and the specific analysis process is as follows:
s1, starting from a large time scale, taking one month of real-time data as a research object according to the real-time power P in the daily power gridiAnalyzing the time distribution characteristics, dividing the time window of the power utilization according to the size of the usage, and defining as follows:
wherein
Is the mean value of the power;
s2, according to the time window obtained in the S1, the power data sequence of the daily real-time power grid under each state window is sorted:
respectively indicate the peak time in day dReal-time power data sequences under an inter-window, a normal time window and a valley time window;
s3, under each state window, taking the number of digits at the same time as the numerical value at the time to obtain the power data { P } of one day under the new overall rulei,h}，{Pi,n}，{Pi,l}；
S4, aiming at the peak time window ThLower power data { Pi,hUsing Daubechies wavelets to carry out waveform decomposition according to L2Orthonormal basis V on (R) subspacem+1Will { Pi,hThe spreading is carried out, and then the spreading is carried out,
wherein the content of the first and second substances,
respectively represent { Pi,hLow-frequency and high-frequency portions, cmk，dmkRespectively representing power { Pi,hThe wavelet coefficients spread over the m-scale;
s5, recombining the low-frequency part of the power to obtain filtered power data
S6, pairing sequences
And
repeating the steps of S3-S5 to obtain new filtered power data
And
s7 according to the power in S101The time window of (2) to arrange the voltage data under the corresponding state window
And
repeating the steps S2-S5 to obtain the filtered voltage data
And
s8, respectively representing power as a polynomial function of voltage in each state window according to the principle that the performance of the power grid is relatively stable in the same state, specifically as follows:
s9, repeating the steps S1-S8 on the real-time reactive data and voltage data sequence on the power grid terminal to obtain polynomial functions of reactive power and voltage under different state windows, wherein the polynomial functions are as follows:
further, the small time scale in step (3) takes 5s as a time interval, and specifically includes the following analysis processes:
s1, and 5S is used as a real-time small time scale to convert the power data { P in the power gridiAnd voltage data { U }iForm a two-dimensional time sequence Xi＝(Pi,Ui) According to EXi，E[(Xi+n-EXi)(Xi-EXi)]The correlation with i is carried out, and the stability of the two-dimensional time sequence is analyzed;
s2, when the two-dimensional time series is unstable, performing difference processing on the two-dimensional time series to make the two-dimensional time series become a stable time series, specifically calculating as follows:
….
s3, differentiating the X d times from the random processiConverting into an autoregressive operator with phi (B) as an n-order to establish XiThe (n, d, m) order of (a) and autoregressive moving average model ARIMA (n, d, m):
wherein
Is an n-order autoregressive coefficient polynomial, and theta (B) is 1-theta1B-θ2B2-…-θmBmIs a moving smoothing coefficient polynomial of order m;
s4 analysis of X according to ARIMA (n, d, m) modeliIdentifies the power { P }iAnd voltage { U }iThe model parameters between;
s5, converting reactive data in the power grid { QiAnd voltage data { U }iForm a new two-dimensional time sequence Yi＝(Qi,Ui) Repeating steps s1-s4, analyzing YiTo obtain the reactive { QiAnd voltage { U }iThe model parameters between.
Furthermore, the method compares the loads under two time scales through real-time data under different time windows of different power grid terminals for analysis, and comprises the steps of establishing a dynamic function and a mathematical model among active power, reactive power and voltage to optimize the resource allocation of the power grid.
Has the advantages that: compared with the prior art, the dynamic management method of the power grid based on the scada system and multiple time scales is used for the real-time dynamic management analysis method of the power distribution network, dynamic change of a time window under a large time scale along with the real-time state of the power grid is realized for the first time, a mathematical model under a small scale is established from the angle of a vector, the dynamic change process of long-time stability and short-time randomness of the real-time state of the power grid can be better described at the same time, the functional relation between real-time power, reactive power and voltage data on a main grid terminal in a source line under the large time scale and the mathematical model under the small time scale can be calculated more quickly and accurately, and a new management analysis method and approach are provided for intelligent regulation and control of the power grid.
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FIG. 1 is a schematic diagram of the structure of the process of the present invention;
FIG. 2 is a flow chart of data processing on a large time scale in the method of the present invention;
FIG. 3 is a flow chart of data analysis on a small time scale in the method of the present invention.
Detailed description of the preferred embodiments
For the purpose of illustrating the technical solutions disclosed in the present invention in detail, the following description is further illustrated with reference to the accompanying drawings and specific examples.
The invention provides a dynamic power grid management method based on a scada system and multiple time scales. With reference to fig. 1-3, the main implementation is as follows:
1. intercepting scada sampling data of the main network terminal in the last month, and establishing dynamic windows of different states of the power grid, which change along with real-time data under a large time scale.
2. And (3) carrying out superposition and filtering processing on the real-time data of the power grid under the same state window, and respectively establishing a function expression among power, reactive power and voltage.
3. From the angle of vectors, a summation autoregressive moving average model ARIMA is established by utilizing a two-dimensional real-time sequence of power and voltage, and model parameters are identified under a small time scale, so that a mathematical model between power data and voltage data on a power grid terminal is obtained.
4. And (5) repeating the step (3) to obtain a mathematical model between the reactive data and the voltage data on the power grid terminal under the small time scale and the vector view angle.
Specifically, the method mainly comprises the following three parts:
step 1: the management analysis method under the large time scale and the steps are as follows:
1. from a large time scale, analyzing the scada system sampling and monitoring data of the last natural month by taking each natural month as a unit according to the real-time power P in the daily power gridi(every 5 seconds), analyzing the time distribution characteristics, and dividing a power use time window according to the size of the use amount:
2. and respectively sorting out real-time power grid data under each state window every day according to the time windows:
3. and according to a majority theorem, filtering the power data under each state window from the angle of probability convergence. The method comprises the following specific steps:
3.1 under each state window, taking the number of digits at the same time as the numerical value at the time to obtain the power data { P) of one day under the new overall rulei,h}，{Pi,n}，{Pi,l}；
3.2 pairs of Peak time Window ThLower power data { Pi,hUsing Daubechies wavelet (N-2) to carry out waveform decomposition according to L2Orthonormal basis V on (R) subspacem+1Will { Pi,hThe spreading is carried out, and then the spreading is carried out,
wherein the content of the first and second substances,
respectively represent { Pi,hLow-frequency and high-frequency portions, cmk，dmkRespectively representing power { Pi,hThe wavelet coefficients spread over the m-scale;
3.3 recombining the low-frequency part of the power to obtain the filtered power data
3.4 for two other state windows
And
repeating the steps of 3.1-3.3 to obtain new filtered power data
And
4. according to the time window of the power, the voltage data under the corresponding state window is arranged
And
repeating the step3 to obtain filtered voltage data
And
5. according to the principle that the performance of the power grid is relatively stable in the same state, power is expressed into a polynomial function of voltage in each state window to obtain a function
And
6. repeating the steps 1-5 on the reactive power and the voltage to obtain a polynomial function of the reactive power and the voltage under different state windows
And
step 2: the management analysis method under the small time scale and the steps are as follows:
1. starting from a real-time scale, the power data { P in the power gridiAnd voltage data { U }iForm a two-dimensional time sequence Xi＝(Pi,Ui) According to EXi，E[(Xi+n-EXi)(Xi-EXi)]The correlation with i is carried out, and the stability of the two-dimensional time sequence is analyzed;
2. when the two-dimensional time series is unstable, the two-dimensional time series is subjected to score checking processing, so that the two-dimensional time series becomes an undetermined time series. Wherein:
….
3. from the angle of random process, differentiating X for d timesiConverting into an autoregressive operator with phi (B) as an n-order to establish Xi(n, d, m) th order sum autoregressive moving average model ofARIMA (n, d, m), where Φ (B) is the smooth, reversible stochastic process of the m-th order moving average operator.
4. Analysis of X according to ARIMA (n, d, m) aboveiTo obtain the power { P }iAnd voltage { U }iThe dynamic evolution law between.
5. Reactive data (Q) in power gridiAnd voltage data { U }iForm a new two-dimensional time sequence Yi＝(Qi,Ui) Repeating steps 1-4, and analyzing YiTo obtain the reactive { QiAnd voltage { U }iThe dynamic evolution law between.
Step3 comprehensive analysis management and optimized scheduling
And synthesizing the analysis results on the main network terminal to obtain an analysis method for the power, the reactive power and the voltage on the source line under multiple time scales, and providing a basis and reference for the stable operation of the power distribution system.
The method provided by the invention establishes dynamic change of time distribution along with the real-time state of the power grid under the large time scale according to the characteristic that the window time length under the large time scale is not fixed with the dynamic operation of the power grid, and can better describe the dynamic evolution rule of the power grid. Research data under a large time scale is reconstructed by the mode of daily real-time data under the same state window, and the influence of random events is effectively avoided. In addition, the invention establishes a random process model of power and voltage, and reactive power and voltage for the first time from the angle of vector under the small time scale; and under the small time scale, analyzing the dynamic evolution relation of power, reactive power and voltage according to a summation autoregressive moving average model of a two-dimensional time sequence to lay a solid foundation for the optimization and scheduling of the power grid.
Claims (5)
1. A dynamic management method of a power grid based on a scada system and multiple time scales is characterized in that: the method includes the steps that real-time data are obtained based on a scada system on a main network terminal in a source line, then dynamic analysis models of a power grid are respectively established with a large time scale of one month and a small time scale of 5 seconds, data under the same state window are overlapped and filtered based on a majority theorem under the large time scale, analysis models are established based on a difference method and an autoregressive moving average model ARIMA under the small time scale, and finally the power grid is managed according to dynamic relations between active power, reactive power and voltage.
2. The dynamic management method of the power grid based on the scada system and the multiple time scales according to claim 1, characterized in that: the management method comprises the following steps:
(1) acquiring scada system sampling data of a natural month on a power distribution network terminal, and establishing dynamic time windows of 3 demand states of peak, common and valley under a large time scale according to time distribution characteristics of real-time data of power and reactive power in a power grid every day;
(2) under a large time scale, overlapping power, reactive power and voltage data under the same state window, performing filtering processing through decomposition and reconstruction of a wavelet packet, and respectively establishing mathematical models of dynamic relations among the power, the reactive power and the voltage in different states;
(3) establishing a two-dimensional time sequence of power and voltage under a small time scale, establishing a summation autoregressive moving average model ARIMA according to a difference method, and identifying model parameters under a real-time scale to obtain a mathematical model between the power and the voltage;
(4) establishing a two-dimensional time sequence of reactive power and voltage based on a small time scale;
(5) and (5) repeating the step (4) to obtain a mathematical model between the reactive power and the voltage under the small time scale.
3. The dynamic management method of the power grid based on the scada system and the multiple time scales according to claim 1, characterized in that: the time scale of the large time scale in the step (2) is one month, and the specific analysis process is as follows:
s1, determining a large time scale, taking one month of real-time data as a research object, and according to the real-time power P in the daily power gridiAnalyzing the time distribution characteristics, and dividing the power usage time according to the power valueThe window, defined as follows:
wherein
Is the mean value of the power;
s2, calculating the power data sequence of the daily real-time power grid under each state window according to the time window obtained in the step S1, wherein the real-time power data sequence under the high peak time window, the common time window and the low valley time window in the day d is
And
s3, under each state window, taking the number of digits at the same time as the numerical value at the time to obtain the power data { P } of one day under the new overall rulei,h}，{Pi,n}，{Pi,l}；
S4, aiming at the peak time window ThLower power data { Pi,hUsing Daubechies wavelets to carry out waveform decomposition according to L2Orthonormal basis V on (R) subspacem+1Will { Pi,hThe spreading is carried out, and then the spreading is carried out,
wherein the content of the first and second substances,
respectively represent { Pi,hLow-frequency and high-frequency portions, cmk，dmkRespectively representing power { Pi,hThe wavelet coefficients of { C };
s5, mixingRecombining the low-frequency part of the power to obtain filtered power data
S6, pairing sequences
And
repeating the steps S3-S5 to obtain new filtered power data
And
s7, calculating the voltage data under the corresponding state window according to the power time window in the step S1
And
repeating the steps S2-S5 to obtain the filtered voltage data
And
s8, according to the principle that the performance of the power grid is relatively stable in the same state, expressing the power into a polynomial function of the voltage in each state window, wherein the specific expression is as follows:
s9, repeating the steps S1-S8 on the real-time reactive data and voltage data sequence on the power grid terminal to obtain a polynomial function of reactive power and voltage under different state windows, which is specifically as follows:
4. the dynamic management method of the power grid based on the scada system and the multiple time scales according to claim 1, characterized in that: the small time scale in the step (4) takes 5s as a time interval, and the method specifically comprises the following analysis process:
s1, and 5S is used as a real-time small time scale to convert the power data { P in the power gridiAnd voltage data { U }iForm a two-dimensional time sequence Xi＝(Pi,Ui) According to EXi，E[(Xi+n-EXi)(Xi-EXi)]Correlation with i, analyzing two-dimensional time sequenceThe stationarity of the columns;
s2, when the two-dimensional time series is not stable, performing difference processing on the two-dimensional time series to make the two-dimensional time series become an undetermined time series, and specifically calculating as follows:
....
s3, differentiating the X d times from the random processiConverting into an autoregressive operator with phi (B) as an n-order to establish XiThe (n, d, m) order of (a) and autoregressive moving average model ARIMA (n, d, m):
wherein
Is an n-order autoregressive coefficient polynomial, and theta (B) is 1-theta1B-θ2B2-…-θmBmIs a moving smoothing coefficient polynomial of order m;
s4 analysis of X according to ARIMA (n, d, m) modeliIdentifies the power { P }iAnd voltage { U }iThe model parameters between;
s5, converting reactive data in the power grid { QiAnd voltage data { U }iForm a new two-dimensional time sequence Yi＝(Qi,Ui) Repeating steps s1-s4, analyzing YiThereby obtainingTo reactive { QiAnd voltage { U }iThe model parameters between.
5. The dynamic management method of the power grid based on the scada system and the multiple time scales according to claim 1, characterized in that: the method comprises the steps of comparing loads under two time scales through real-time data under different time windows of different power grid terminals, and analyzing, wherein dynamic functions and mathematical models among active power, reactive power and voltage are established to optimize the resource allocation of the power grid.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116070782A (en) * | 2023-03-06 | 2023-05-05 | 深圳市三和电力科技有限公司 | Big data-based energy reserve management method and system |
| CN116070782B (en) * | 2023-03-06 | 2023-07-04 | 深圳市三和电力科技有限公司 | Big data-based energy reserve management method and system |
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